This paper proposes a new hybrid stochastic training algorithm using the recently proposed grasshopper optimization algorithm (GOA) for multilayer perceptrons (MLPs) neural networks. The GOA algorithm is an emerging technique with a high potential in tackling optimization problems based on its flexible and adaptive searching mechanisms. It can demonstrate a satisfactory performance by escaping from local optima and balancing the exploration and exploitation trends. The proposed GOAMLP model is then applied to five important datasets: breast cancer, parkinson, diabetes, coronary heart disease, and orthopedic patients. The results are deeply validated in comparison with eight recent and well-regarded algorithms qualitatively and quantitatively. It is shown and proved that the proposed stochastic training algorithm GOAMLP is substantially beneficial in improving the classification rate of MLPs.

An efficient hybrid multilayer perceptron neural network with grasshopper optimization

Machine Learning and Artificial Intelligence (AI) more broadly have great immediate and future potential for transforming almost all aspects of medicine. However, in many applications, even outside medicine, a lack of transparency in AI applications has become increasingly problematic. This is particularly pronounced where users need to interpret the output of AI systems. Explainable AI (XAI) provides a rationale that allows users to understand why a system has produced a given output. The output can then be interpreted within a given context. One area that is in great need of XAI is that of Clinical Decision Support Systems (CDSSs). These systems support medical practitioners in their clinic decision-making and in the absence of explainability may lead to issues of under or over-reliance. Providing explanations for how recommendations are arrived at will allow practitioners to make more nuanced, and in some cases, life-saving decisions. The need for XAI in CDSS, and the medical field in general, is amplified by the need for ethical and fair decision-making and the fact that AI trained with historical data can be a reinforcement agent of historical actions and biases that should be uncovered. We performed a systematic literature review of work to-date in the application of XAI in CDSS. Tabular data processing XAI-enabled systems are the most common, while XAI-enabled CDSS for text analysis are the least common in literature. There is more interest in developers for the provision of local explanations, while there was almost a balance between post-hoc and ante-hoc explanations, as well as between model-specific and model-agnostic techniques. Studies reported benefits of the use of XAI such as the fact that it could enhance decision confidence for clinicians, or generate the hypothesis about causality, which ultimately leads to increased trustworthiness and acceptability of the system and potential for its incorporation in the clinical workflow. However, we found an overall distinct lack of application of XAI in the context of CDSS and, in particular, a lack of user studies exploring the needs of clinicians. We propose some guidelines for the implementation of XAI in CDSS and explore some opportunities, challenges, and future research needs.

Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review

A bat algorithm (BA) is a heuristic algorithm that operates by imitating the echolocation behavior of bats to perform global optimization. The BA is widely used in various optimization problems because of its excellent performance. In the bat algorithm, the global search capability is determined by the parameter loudness and frequency. However, experiments show that each operator in the algorithm can only improve the performance of the algorithm at a certain time. In this paper, a novel bat algorithm with multiple strategies coupling (mixBA) is proposed to solve this problem. To prove the effectiveness of the algorithm, we compared it with CEC2013 benchmarks test suits. Furthermore, the Wilcoxon and Friedman tests were conducted to distinguish the differences between it and other algorithms. The results prove that the proposed algorithm is significantly superior to others on the majority of benchmark functions.

https://www.mdpi.com/2227-7390/7/2/135/pdf

A Novel Bat Algorithm with Multiple Strategies Coupling for Numerical Optimization

Real-time anomaly detection based on long short-Term memory and Gaussian Mixture Model

This paper applies the Logical Analysis of Data (LAD) to detect and diagnose faults in industrial chemical processes. This machine learning classification technique discovers hidden knowledge in industrial datasets by revealing interpretable patterns, which are linked to underlying physical phenomena. The patterns are then combined to build a decision model that serves to diagnose faults during the process operation, and to explain the potential causes of these faults. LAD is applied to two case studies, selected to exemplify the difficulty in interpreting faults in complex chemical processes. The first case study is the Tennessee Eastman Process (TEP), a well-known benchmark problem in the field of process monitoring and control that uses simulated data. The second one uses a real dataset from a black liquor recovery boiler in a pulp mill. The results are compared to those obtained by other common machine learning techniques, namely artificial neural networks (ANN), Decision Tree (DT), Random Forest (RF), k nearest neighbors (kNN), quadratic discriminant analysis (QDA) and support vector machine (SVM). In addition to its explanatory power, the results show that LAD's performance is comparable to the most accurate techniques.

Fault diagnosis in industrial chemical processes using interpretable patterns based on Logical Analysis of Data

Early Hospital Mortality Prediction using Vital Signals.

Event handlers have wide range of applications such as medical assistant systems and fire suppression systems. These systems try to provide accurate responses based on the least information. Support vector data description (SVDD) is one of the appropriate tools for such detections, which should handle lack of information. Therefore, many efforts have been done to improve SVDD. Unfortunately, the existing descriptors suffer from weak data characteristic in sparse data sets and their tuning parameters are organized improperly. These issues cause reduction of accuracy in event handlers when they are faced with data shortage. Therefore, we propose automatic support vector data description (ASVDD) based on both validation degree, which is originated from fuzzy rough set to discover data characteristic, and assigning effective values for tuning parameters by chaotic bat algorithm. To evaluate the performance of ASVDD, several experiments have been conducted on various data sets of UCI repository. The experimental results demonstrate superiority of the proposed method over state-of-the-art ones in terms of classification accuracy and AUC. In order to prove meaningful distinction between the accuracy results of the proposed method and the leading-edge ones, the Wilcoxon statistical test has been conducted.

Automatic support vector data description

Despite emerging of Web 2.0 applications and increasing requirements to well-behaved Web robots, malicious ones can reveal irreparable risks for Web sites. Regardless of behavior of Web robots, they may occupy bandwidth and reduce performance of Web servers. In spite of many prestigious researches trying to characterize Web visitors and classify them, there is a lack of concentration on feature selection to dynamically choose attributes used to describe Web sessions. On the other hand, depending on an accurate clustering technique, which can deal with huge number of samples in a reasonable amount of time, is practically important. Therefore, in this paper, a new algorithm, fuzzy rough set–Web robot detection (FRS-WRD), is proposed based on fuzzy rough set theory to better characterize and cluster Web visitors of three real Web sites. External evaluations show that in contrast to state-of-the-art algorithms, FRS-WRD achieves better results in terms of G-mean 95%, Jaccard 88%, entropy 0.36, and finally, purity 96%. Moreover, according to confusion matrixes, it can better detect malicious Web visitors.

Detection of Web site visitors based on fuzzy rough sets

We propose a method called SMART (Soft computing for MAlicious RoboT detection).The method detects benign and malicious robots, and human visitors to a web server.SMART selects its features on a particular web server by fuzzy rough set theory.A graph-based clustering algorithm classifies sessions into the three agent types.Analyses on web logs suggest state-of-the-art results to detect both robot types. The accurate detection of web robot sessions from a web server log is essential to take accurate traffic-level measurements and to protect the performance and privacy of information on a Web server. Moreover, the irrecoverable risks of visits from malicious robots that intentionally try to evade web server intrusion detection systems, covering-up their visits with fabricated fields in their http request packets, cannot be ignored. To separate both types of robots from humans in practice, analysts turn to heuristic methods or state-of-the-art soft computing approaches that have only been tuned to the specification of a kind of web server. Noting that the landscape of web robot agents is ever changing, and that behavioral patterns and characteristics vary across different web servers, both options are lacking. To overcome this challenge, this paper presents SMART, a soft computing system that simultaneously detects benign and malicious types of robot agents from web server logs and can automatically adapt to the session characteristics of a web server. The results of experiments over some access log file servers, each servicing different domains of the web, demonstrate outperformance of the proposed method on state-of-the-art ones for benign and malicious robot detection.

Reza Sadeghi

Papers

Early Hospital Mortality Prediction using Vital Signals.

Automatic support vector data description

Detection of Web site visitors based on fuzzy rough sets

A soft computing approach for benign and malicious web robot detection

Weighted support vector data description based on chaotic bat algorithm